Computational Intelligence最新文献

RETRACTION: J.D. Kharibam, T. Khelchandra, “ Automatic Speaker Recognition from Speech Signal Using Bidirectional Long Short-term Memory Recurrent Neural Network,” Computational Intelligence 39 no. 2 (2023): 170–193, https://doi.org/10.1111/coin.12278.

The above article, published online on 23 January 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: G. Premalatha, P. V. Chandramani, “ Improved Gait Recognition through Gait Energy mage Partitioning,” Computational Intelligence 36 no. 3 (2020): 1261–1274, https://doi.org/10.1111/coin.12340.

The above article, published online on 22 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: S. Narasimhan, M. Arunachalam, “ Bio-PUF-MAC Authenticated Encryption for Iris Biometrics,” Computational Intelligence 36 no. 3 (2020): 1221–124, https://doi.org/10.1111/coin.12332.

The above article, published online on 27 May 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.

Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in-depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in-depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine-tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher-order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high-accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.

A large amount of data have been published online in textual format for the last decade because of the advancement of information and communication technologies. This is an open challenge to organize and classify large amounts of textual data automatically, especially for a language that has limited resources available online. In this study, two types of approaches are adopted for experiments. First one is a traditional strategy that uses six (06) classical state-of-the-art classification models (1. decision tree (DT), 2. logistic regression (LR), 3. support vector machine (SVM), 4. k-nearest neighbour (k-NN), 5. Naive Bayes (NB), and 6. random forest (RF)) along with two (02) ensemble methods (1. Adaboost and 2. gradient boosting (GB)) and second modeling technique is our proposed voting based ensembling scheme. Models are trained on a 75-25 split where 75% of data is used for training and 25% for testing. The evaluation of the classification models is carried out based on accuracy, precision, recall, and F1-score indexes. The experimental outcomes witnessed that for the traditional approach, gradient boosting outperformed for the limited resource language with 98.08% F1-score, while SVM performed better (97.34% F1-score) for the resource-rich language.

Brain-computer interface (BCI) is a growing area of research in human-computer interaction (HCI), where its potential ranges from medicine to entertainment. It intends to manage various assistive technologies through the utilization of brain signals. This technology acquires and interprets brain signals before sending them to a connected device, which generates controls based on the obtained signals. Emotion-based mental state categorization employing electroencephalogram (EEG) signals is an emerging method of BCI application. However, EEG signals comprise artifacts and redundant or noisy information from the subject, equipment, and external environment. Also, the EEG signals have a low spatial resolution (physical location of the activity within the brain) but a high temporal resolution (millisecond level). Therefore, artifact removal, feature extraction, and classification of EEG signals are challenging. This work proposed a novel approach called Extended Independent Component Analysis (E-ICA) for artifact removal from EEG signals. A Multi-class Common Spatial Pattern (M-CSP) is proposed for feature extraction. A Bidirectional long short-term memory (BiLSTM) network is proposed to improve the classification of EEG signals and fine-tune its parameters. This study leverages the Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to validate the model's performance. This dataset includes EEG recordings annotated with emotional attributes such as valence, arousal, dominance, and liking. After conducting several experiments, the proposed approach achieves a high classification accuracy of 94.61% and outperforms state-of-the-art works. The proposed approach can be successfully integrated into BCI systems for real-time emotion identification in healthcare and user engagement detection in gaming environments.